Temperature-Sensitive Point Selection and Thermal Error Model Adaptive Update Method of CNC Machine Tools

The thermal error of CNC machine tools can be reduced by compensation, where a thermal error model is required to provide compensation values. The thermal error model adaptive update method can correct the thermal error model by supplementing new data, which fundamentally solves the problem of model robustness. Certain problems associated with this method in temperature-sensitive point (TSP) selection and model update algorithms are investigated in this study. It was found that when the TSPs were selected frequently, the selection results may be different, that is, there was a variability problem in TSPs. Further, it was found that the variability of TSPs is mainly due to some problems with the TSP selection method, (1) the conflict between the collinearity among TSPs and the correlation of TSPs with thermal error is ignored, (2) the stability of the correlation is not considered. Then, a stable TSP selection method that can choose more stable TSPs with less variability was proposed. For the model update algorithm, this study proposed a novel regression algorithm which could effectively combine the new data with the old model. It has advantages for a model update, (1) fewer data are needed for the model update, (2) the model accuracy is greatly improved. The effectiveness of the proposed method was verified by 20 batches of thermal error measurement experiments in the real cutting state of the machine tool

Looking and Listening: Audio Guided Text Recognition

Text recognition in the wild is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest vision and language processing are effective for scene text recognition. Yet, solving edit errors such as add, delete, or replace is still the main challenge for existing approaches. In fact, the content of the text and its audio are naturally corresponding to each other, i.e., a single character error may result in a clear different pronunciation. In this paper, we propose the AudioOCR, a simple yet effective probabilistic audio decoder for mel spectrogram sequence prediction to guide the scene text recognition, which only participates in the training phase and brings no extra cost during the inference stage. The underlying principle of AudioOCR can be easily applied to the existing approaches. Experiments using 7 previous scene text recognition methods on 12 existing regular, irregular, and occluded benchmarks demonstrate our proposed method can bring consistent improvement. More importantly, through our experimentation, we show that AudioOCR possesses a generalizability that extends to more challenging scenarios, including recognizing non-English text, out-of-vocabulary words, and text with various accents. Code will be available at https://github.com/wenwenyu/AudioOCR

Enantiomeric Discrimination by Surface- Enhanced Raman Scattering- Chiral Anisotropy of Chiral Nanostructured Gold Films

A surface- enhanced Raman scattering- chiral anisotropy (SERS- ChA) effect is reported that combines chiral discrimination and surface Raman scattering enhancement on chiral nanostructured Au films (CNAFs) equipped in the normal Raman scattering Spectrometer. The CNAFs provided remarkably higher enhancement factors of Raman scattering (EFs) for particular enantiomers, and the SERS intensity was proportional to the enantiomeric excesses (ee) values. Except for molecules with mesomeric species, all of the tested enantiomers exhibited high SERS- ChA asymmetry factors (g), ranging between 1.34 and 1.99 regardless of polarities, sizes, chromophores, concentrations and ee. The effect might be attributed to selective resonance coupling between the induced electric and magnetic dipoles associated with enantiomers and chiral plasmonic modes of CNAFs.Absolution by SERS: A surface- enhanced Raman scattering chiral anisotropy effect is presented that combines chiral discrimination and surface Raman scattering enhancement on chiral nanostructured Au films. It is applied in the normal Raman scattering system to identify the absolute configuration and composition of enantiomers, overcoming disadvantages of polarimeter systems and chromatography.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156417/3/anie202006486-sup-0001-misc_information.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156417/2/anie202006486_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156417/1/anie202006486.pd

Enantiomeric Discrimination by Surface- Enhanced Raman Scattering- Chiral Anisotropy of Chiral Nanostructured Gold Films

A surface- enhanced Raman scattering- chiral anisotropy (SERS- ChA) effect is reported that combines chiral discrimination and surface Raman scattering enhancement on chiral nanostructured Au films (CNAFs) equipped in the normal Raman scattering Spectrometer. The CNAFs provided remarkably higher enhancement factors of Raman scattering (EFs) for particular enantiomers, and the SERS intensity was proportional to the enantiomeric excesses (ee) values. Except for molecules with mesomeric species, all of the tested enantiomers exhibited high SERS- ChA asymmetry factors (g), ranging between 1.34 and 1.99 regardless of polarities, sizes, chromophores, concentrations and ee. The effect might be attributed to selective resonance coupling between the induced electric and magnetic dipoles associated with enantiomers and chiral plasmonic modes of CNAFs.Absolution by SERS: A surface- enhanced Raman scattering chiral anisotropy effect is presented that combines chiral discrimination and surface Raman scattering enhancement on chiral nanostructured Au films. It is applied in the normal Raman scattering system to identify the absolute configuration and composition of enantiomers, overcoming disadvantages of polarimeter systems and chromatography.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156470/3/ange202006486_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156470/2/ange202006486.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156470/1/ange202006486-sup-0001-misc_information.pd

Genomic Analyses Reveal Mutational Signatures and Frequently Altered Genes in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide and the fourth most lethal cancer in China. However, although genomic studies have identified some mutations associated with ESCC, we know little of the mutational processes responsible. To identify genome-wide mutational signatures, we performed either whole-genome sequencing (WGS) or whole-exome sequencing (WES) on 104 ESCC individuals and combined our data with those of 88 previously reported samples. An APOBEC-mediated mutational signature in 47% of 192 tumors suggests that APOBEC-catalyzed deamination provides a source of DNA damage in ESCC. Moreover, PIK3CA hotspot mutations (c.1624G>A [p.Glu542Lys] and c.1633G>A [p.Glu545Lys]) were enriched in APOBEC-signature tumors, and no smoking-associated signature was observed in ESCC. In the samples analyzed by WGS, we identified focal (<100 kb) amplifications of CBX4 and CBX8. In our combined cohort, we identified frequent inactivating mutations in AJUBA, ZNF750, and PTCH1 and the chromatin-remodeling genes CREBBP and BAP1, in addition to known mutations. Functional analyses suggest roles for several genes (CBX4, CBX8, AJUBA, and ZNF750) in ESCC. Notably, high activity of hedgehog signaling and the PI3K pathway in approximately 60% of 104 ESCC tumors indicates that therapies targeting these pathways might be particularly promising strategies for ESCC. Collectively, our data provide comprehensive insights into the mutational signatures of ESCC and identify markers for early diagnosis and potential therapeutic targets